Introduction: The National Early Warning Score (NEWS/2) system was developed to enable the detection and early intervention of patients at risk of clinical deterioration. It has demonstrated good accuracy in identifying imminent critical outcomes but has limitations in its applicability to various patient types and its ability to predict upcoming deterioration beyond 24 hours. Various studies have attempted to improve its predictive accuracy and clinical utility by modifying or adding variables to the standard NEWS/2 system. The purpose of this scoping review is to identify modifications to the NEWS and NEWS2 systems (eg, the inclusion of additional patient demographic, physiological or other characteristics) and how those modifications influence predictive accuracy to provide an evidence base for subsequent improvement of the system.
Methods And Analysis: The review will be structured using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and the Population, Intervention, Comparator, Outcome, and Study frameworks. Six databases (PubMed, ScienceDirect, Embase, CINAHL, Web of Science and Cochrane Library) will be searched in April 2024 for articles published in English. Article screening and data extraction will be conducted by two independent reviewers, with any conflicts resolved by discussion. The analysis will be descriptive to provide a summary of modifications identified and their influence on the predictive accuracy of NEWS/NEWS 2.
Ethics And Dissemination: Ethical approval is not required as data will be obtained from already published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal.
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http://dx.doi.org/10.1136/bmjopen-2024-089061 | DOI Listing |
JCI Insight
January 2025
Medical Oncology Department, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands.
Background: Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICI) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy.
Methods: Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (N=88) and immunotranscriptome (N=79) analyses.
J Med Chem
January 2025
Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space of real-world scenarios. Artificial intelligence (AI) has revolutionized retrosynthesis prediction in recent decades, significantly increasing the accuracy and diversity of predictions for target compounds.
View Article and Find Full Text PDFJAMA Oncol
January 2025
Department of Urology, Seoul National University Hospital, Seoul, Republic of Korea.
Importance: An accurate noninvasive biomarker test is needed for the early diagnosis of bladder cancer.
Objective: To evaluate the performance of a urinary DNA methylation test (PENK methylation) and compare its diagnostic accuracy with that of the nuclear matrix protein 22 (NMP22) test or urine cytology test.
Design, Setting, And Participants: In this prospective multicenter study at 10 sites in the Republic of Korea, individuals 40 years and older with hematuria undergoing cystoscopy within 3 months between March 11, 2022, and May 30, 2024, participated.
Trop Anim Health Prod
January 2025
Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu City, Sichuan Province, 610041, China.
Background: Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade.
Methods: This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts.
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